Weekly Exercises #5

library(tidyverse)     # for data cleaning and plotting

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## ✓ tibble  3.0.3     ✓ dplyr   1.0.0
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0

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library(googlesheets4) # for reading googlesheet data
library(lubridate)     # for date manipulation

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## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
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##     date, intersect, setdiff, union

library(openintro)     # for the abbr2state() function

## Loading required package: airports

## Loading required package: cherryblossom

## Loading required package: usdata

library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data

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library(ggmap)         # for mapping points on maps

## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.

## Please cite ggmap if you use it! See citation("ggmap") for details.

library(gplots)        # for col2hex() function

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## Attaching package: 'gplots'

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library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data

## Linking to GEOS 3.8.1, GDAL 3.1.1, PROJ 6.3.1

library(leaflet)       # for highly customizable mapping
library(ggthemes)      # for more themes (including theme_map())
library(plotly)        # for the ggplotly() - basic interactivity

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library(gganimate)     # for adding animation layers to ggplots
library(transformr)    # for "tweening" (gganimate)

## 
## Attaching package: 'transformr'

## The following object is masked from 'package:sf':
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library(gifski)        # need the library for creating gifs but don't need to load each time
library(shiny)         # for creating interactive apps
gs4_deauth()           # To not have to authorize each time you knit.
theme_set(theme_minimal())
library(ggridges)

# SNCF Train data
small_trains <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-02-26/small_trains.csv") 

## 
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   year = col_double(),
##   month = col_double(),
##   service = col_character(),
##   departure_station = col_character(),
##   arrival_station = col_character(),
##   journey_time_avg = col_double(),
##   total_num_trips = col_double(),
##   avg_delay_all_departing = col_double(),
##   avg_delay_all_arriving = col_double(),
##   num_late_at_departure = col_double(),
##   num_arriving_late = col_double(),
##   delay_cause = col_character(),
##   delayed_number = col_double()
## )

# Lisa's garden data
garden_harvest <- read_sheet("https://docs.google.com/spreadsheets/d/1DekSazCzKqPS2jnGhKue7tLxRU3GVL1oxi-4bEM5IWw/edit?usp=sharing") %>% 
  mutate(date = ymd(date))

## Reading from "2020_harvest"

## Range "Sheet1"

# Lisa's Mallorca cycling data
mallorca_bike_day7 <- read_csv("https://www.dropbox.com/s/zc6jan4ltmjtvy0/mallorca_bike_day7.csv?dl=1") %>% 
  select(1:4, speed)

## 
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   lon = col_double(),
##   lat = col_double(),
##   ele = col_double(),
##   time = col_datetime(format = ""),
##   extensions = col_double(),
##   ele.num = col_double(),
##   date = col_date(format = ""),
##   hrminsec = col_datetime(format = ""),
##   time_hr = col_double(),
##   dist_km = col_double(),
##   speed = col_double()
## )

# Heather Lendway's Ironman 70.3 Pan Am championships Panama data
panama_swim <- read_csv("https://raw.githubusercontent.com/llendway/gps-data/master/data/panama_swim_20160131.csv")

## 
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   lon = col_double(),
##   lat = col_double(),
##   time = col_datetime(format = ""),
##   extensions = col_double(),
##   ele = col_logical(),
##   event = col_character(),
##   date = col_date(format = ""),
##   hrminsec = col_datetime(format = "")
## )

panama_bike <- read_csv("https://raw.githubusercontent.com/llendway/gps-data/master/data/panama_bike_20160131.csv")

## 
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   lon = col_double(),
##   lat = col_double(),
##   ele = col_double(),
##   time = col_datetime(format = ""),
##   extensions = col_double(),
##   event = col_character(),
##   date = col_date(format = ""),
##   hrminsec = col_datetime(format = "")
## )

panama_run <- read_csv("https://raw.githubusercontent.com/llendway/gps-data/master/data/panama_run_20160131.csv")

## 
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   lon = col_double(),
##   lat = col_double(),
##   ele = col_double(),
##   time = col_datetime(format = ""),
##   extensions = col_double(),
##   event = col_character(),
##   date = col_date(format = ""),
##   hrminsec = col_datetime(format = "")
## )

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

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## cols(
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##   fips = col_character(),
##   cases = col_double(),
##   deaths = col_double()
## )

Put your homework on GitHub!

Go here or to previous homework to remind yourself how to get set up.

Once your repository is created, you should always open your project rather than just opening an .Rmd file. You can do that by either clicking on the .Rproj file in your repository folder on your computer. Or, by going to the upper right hand corner in R Studio and clicking the arrow next to where it says Project: (None). You should see your project come up in that list if you’ve used it recently. You could also go to File –> Open Project and navigate to your .Rproj file.

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• NEW!! With animated graphs, add eval=FALSE to the code chunk that creates the animation and saves it using anim_save(). Add another code chunk to reread the gif back into the file. See the tutorial for help.

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises from tutorial

1. Choose 2 graphs you have created for ANY assignment in this class and add interactivity using the ggplotly() function.

ikea <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-03/ikea.csv')

## Warning: Missing column names filled in: 'X1' [1]

## 
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   X1 = col_double(),
##   item_id = col_double(),
##   name = col_character(),
##   category = col_character(),
##   price = col_double(),
##   old_price = col_character(),
##   sellable_online = col_logical(),
##   link = col_character(),
##   other_colors = col_character(),
##   short_description = col_character(),
##   designer = col_character(),
##   depth = col_double(),
##   height = col_double(),
##   width = col_double()
## )

new_ikea <- filter(ikea, grepl("0", designer, fixed = FALSE)) %>% 
  select(designer) %>% 
  mutate()

proper_ikea <- ikea[!(ikea$designer %in% new_ikea$designer),] %>% 
  select(name, category, price, designer) %>% 
  filter( price > "50000.0") %>% 
  summarise(price, category, designer) %>% 
  arrange(price) %>% 
  na.omit

top_priceIkea <- proper_ikea[order(-proper_ikea$price),] %>% 
  head(proper_ikea, n = 100)

top_ikea_graph <- top_priceIkea %>% 
  arrange(price) %>% 
  mutate( category = fct_reorder(category, price)) %>% 
  ggplot(aes(x = category, y = price, fill = category)) +
  theme(axis.title.x=element_blank(), axis.text.x=element_blank(), plot.title = element_text(hjust = 0.5, size = 18, face = "bold"), plot.subtitle = element_text(hjust = 0.5, face = "italic")) +
  labs(fill = "Appliance Type", title = "Most Expensive Appliances at Ikea", subtitle = "Macalester Intro to Data science Tidy Tuesday", y = "Recent Price in Saudi Riyals") +
  geom_boxplot(color = "darkgray")


ggplotly(top_ikea_graph,
         tooltip = c("text", "x"))

garden_harvest <- read_sheet("https://docs.google.com/spreadsheets/d/1DekSazCzKqPS2jnGhKue7tLxRU3GVL1oxi-4bEM5IWw/edit?usp=sharing") %>%
  mutate(date = ymd(date))

## Reading from "2020_harvest"

## Range "Sheet1"

garden_graph <- garden_harvest %>% 
  filter(vegetable == "lettuce") %>% 
  ggplot(aes(y = weight, x = date,  fill = variety)) +
  geom_col(color = "black") +
  labs(title = "Distribution of lettuce harvests", caption = "Data from Lisa Lenway", y = "Weight in grams", x = "Date") +
  theme_dark() + 
  scale_fill_manual(values=c("gold", "dark green", "red", "light blue", "purple")) + 
  theme(legend.position = "bottom")

ggplotly(garden_graph,
         tooltip = c("text", "x"))

2. Use animation to tell an interesting story with the small_trains dataset that contains data from the SNCF (National Society of French Railways). These are Tidy Tuesday data! Read more about it here.

small_trains %>%
  group_by(service) %>%
  filter(!is.na(service)) %>%
  filter(departure_station %in% c("ZURICH", "PARIS LYON"))

saved_trains <-small_trains %>% 
  ggplot(aes(x = num_late_at_departure, 
             y = service)) +
  geom_density_ridges() +
  transition_states(year) +
  labs(title = "Departing Delay by French Train Services",
       x = "Average Delay for Departing Trains (Minutes)",
       y = "Train Service",
       subtitle = "Moving to {next_state}") 

anim_save("trains2.gif", saved_trains)

## Picking joint bandwidth of 3.94

Garden data

3. In this exercise, you will create a stacked area plot that reveals itself over time (see the geom_area() examples here). You will look at cumulative harvest of tomato varieties over time. You should do the following:

• From the garden_harvest data, filter the data to the tomatoes and find the daily harvest in pounds for each variety.
  
• Then, for each variety, find the cumulative harvest in pounds.
  
• Use the data you just made to create a static cumulative harvest area plot, with the areas filled with different colors for each vegetable and arranged (HINT: fct_reorder()) from most to least harvested (most on the bottom).
  
• Add animation to reveal the plot over date.

I have started the code for you below. The complete() function creates a row for all unique date/variety combinations. If a variety is not harvested on one of the harvest dates in the dataset, it is filled with a value of 0.

gardenHarv_ani <- garden_harvest %>%
  filter(vegetable == "tomatoes") %>%
  complete(variety, 
           date = seq.Date(min(date), 
                           max(date), 
                           by="day")) %>%
  select(-c(vegetable, units)) %>%
  mutate(weight = replace_na(weight, 0)) %>%
  group_by(variety, date) %>%
  summarize(daily_harvest_lb = sum(weight)*0.00220462) %>%
  mutate(cumsum_daily_harvest_lb = cumsum(daily_harvest_lb)) %>%
  select(-daily_harvest_lb) %>%
  ggplot() +
    geom_area(aes(x = date, 
                  y = cumsum_daily_harvest_lb, 
                  fill = variety), 
              position = position_stack()) +
    transition_reveal(date) +
    labs(title = "Cumulative Tomato Harvest Variety by Time",
       x = "Date",
       y = "Cumulative Daily Harvest in Lb",
       subtitle = "Moving to {frame_along}") 

## `summarise()` regrouping output by 'variety' (override with `.groups` argument)

gardenHarv_ani

anim_save("GardenHarv.gif", gardenHarv_ani)

Maps, animation, and movement!

4. Map my mallorca_bike_day7 bike ride using animation! Requirements:

• Plot on a map using ggmap.
  
• Show “current” location with a red point.
• Show path up until the current point.
  
• Color the path according to elevation.
  
• Show the time in the subtitle.
  
• CHALLENGE: use the ggimage package and geom_image to add a bike image instead of a red point. You can use this image. See here for an example.
• Add something of your own! And comment on if you prefer this to the static map and why or why not.

mallorca_map <- get_stamenmap(
    bbox = c(left = 2.28, bottom = 39.41, right = 3.03, top = 39.8), 
    maptype = "terrain",
    zoom = 11) 

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ggmap(mallorca_map) +
  geom_point(data = mallorca_bike_day7, 
             aes(x = lon, y = lat),
             color = "blue", size = .5) +
  geom_path(data = mallorca_bike_day7, 
             aes(x = lon, y = lat, color = ele),
             size = .5) +
  labs(title = "Lisa Mallorca Bike Trail",
       subtitle = "Time: {frame_along}") +
  transition_reveal(time) +
  scale_color_viridis_c() +
  theme_map() +
  theme(legend.background = element_blank())

5. In this exercise, you get to meet my sister, Heather! She is a proud Mac grad, currently works as a Data Scientist at 3M where she uses R everyday, and for a few years (while still holding a full-time job) she was a pro triathlete. You are going to map one of her races. The data from each discipline of the Ironman 70.3 Pan Am championships, Panama is in a separate file - panama_swim, panama_bike, and panama_run. Create a similar map to the one you created with my cycling data. You will need to make some small changes: 1. combine the files (HINT: bind_rows(), 2. make the leading dot a different color depending on the event (for an extra challenge, make it a different image using `geom_image()!), 3. CHALLENGE (optional): color by speed, which you will need to compute on your own from the data. You can read Heather’s race report here. She is also in the Macalester Athletics Hall of Fame and still has records at the pool.

total_trail <- panama_swim %>%
  bind_rows(list(panama_run, panama_bike)) 
  
panama_map <- get_stamenmap(
  bbox = c(left = -79.56, bottom = 8.88, right = -79.41, top = 9.001),
  maptype = "terrain",
  zoom = 13
)

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ggmap(panama_map) +
  geom_point(data = total_trail, 
             aes(x = lon, y = lat, color = event, shape = event),
             size = 2) +
  geom_path(data = total_trail,
            aes(x = lon, y = lat, color = event),
            alpha = 0.7, size = 0.5) +
  labs(title = "Ironman 70.3 Pan Am Championship Trail",
       subtitle = "Time: {frame_along}") +
  scale_color_viridis_d() +
  theme_map() +
  theme(legend.background = element_blank()) +
  transition_reveal(time)

COVID-19 data

6. In this exercise, you are going to replicate many of the features in this visualization by Aitish Bhatia but include all US states. Requirements:

• Create a new variable that computes the number of new cases in the past week (HINT: use the lag() function you’ve used in a previous set of exercises). Replace missing values with 0’s using replace_na().
  
• Filter the data to omit rows where the cumulative case counts are less than 20.
  
• Create a static plot with cumulative cases on the x-axis and new cases in the past 7 days on the y-axis. Connect the points for each state over time. HINTS: use geom_path() and add a group aesthetic. Put the x and y axis on the log scale and make the tick labels look nice - scales::comma is one option. This plot will look pretty ugly as is.
• Animate the plot to reveal the pattern by date. Display the date as the subtitle. Add a leading point to each state’s line (geom_point()) and add the state name as a label (geom_text() - you should look at the check_overlap argument).
  
• Use the animate() function to have 200 frames in your animation and make it 30 seconds long.
• Comment on what you observe.

Covid_cases_states <- covid19 %>%
  group_by(state) %>%
  mutate(lag7 = lag(cases, 7, order_by = date)) %>%
  replace_na(list(lag7 = 0)) %>%
  mutate(cases_past_week = cases - lag7) %>%
  filter(cases >= 20) %>%
  
  ggplot(aes(x = cases, 
             y = cases_past_week, 
             group = state)) +
  geom_point(color = "pink") +
  geom_path(color = "light gray") +
  geom_text(aes(label = state), 
            check_overlap = TRUE) +
  scale_x_log10(labels = scales::comma) +
  scale_y_log10(labels = scales::comma) +
  labs(
    title = "Trajectory of US COVID-19 Confirmed Cases",
    x = "Total Confirmed Cases",
    y = "New Confirmed Cases (in the Past Week)",
    subtitle = "Date: {frame_along}"
  ) +
  theme(legend.position = "none") +
  transition_reveal(date) -> covid19trajectory_gganim


animate(covid19trajectory_gganim,
        nframes = 200,
        duration = 30)

## Warning in self$trans$transform(x): NaNs produced

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anim_save("covidcases.gif")

7. In this exercise you will animate a map of the US, showing how cumulative COVID-19 cases per 10,000 residents has changed over time. This is similar to exercises 11 & 12 from the previous exercises, with the added animation! So, in the end, you should have something like the static map you made there, but animated over all the days. Put date in the subtitle. Comment on what you see. The code below gives the population estimates for each state. Because there are so many dates, you are going to only do the animation for all Fridays. So, use wday() to create a day of week variable and filter to all the Fridays. HINT: use group = date in aes().

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))

## 
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   state = col_character(),
##   est_pop_2018 = col_double()
## )

covid19_population <-
  covid19 %>% 
  mutate(state = str_to_lower(state)) %>%
  left_join(census_pop_est_2018,
            by = "state") %>% 
  group_by(state, est_pop_2018, date) %>%
  summarize(cumulative_cases = max(cases)) %>%
  mutate(cases_per_10000 = (cumulative_cases/est_pop_2018)*10000)

## `summarise()` regrouping output by 'state', 'est_pop_2018' (override with `.groups` argument)

states_map <- map_data("state")

covid19_map <- covid19_population %>% 
  mutate(state = str_to_lower(state), 
         weekday = wday(date, label=TRUE)) %>%
  filter(weekday == "Fri") %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state, 
               fill = cases_per_10000, 
               group = date)) +
  expand_limits(x = states_map$long, 
                y = states_map$lat) + 
  labs(title = "Cumulative COVID-19 cases per 10,000 people in the United States of America") +
  theme(legend.background = element_blank()) + 
  theme_map() +
  scale_fill_viridis_c() +
  transition_states(date, 
                    transition_length = 0) +
  labs(subtitle = "Moving to {next_state}")

animate(covid19_map, duration = 30)

anim_save("covid19.gif", covid19_map)

GitHub link

9. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 05_exercises.Rmd, provide a link to the 05_exercises.md file, which is the one that will be most readable on GitHub. If that file isn’t very readable, then provide a link to your main GitHub page.

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?